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(1) Background: Human detection and tracking are critical tasks for assistive autonomous robots, particularly in ensuring safe and efficient human–robot interaction in indoor environments. The increasing need for personal assistance among the elderly and people with disabilities has led to the development of innovative robotic systems. (2) Methods: This research presents a lightweight two-layer control architecture for a human-following robot, integrating a fuzzy behavior-based control system with low-level embedded controllers. The system uses an RGB-D sensor to capture distance and angular data, processed by a fuzzy controller to generate speed set-points for the robot's motors. The low-level control layer was developed using pole placement and internal model control (IMC) methods. (3) Results: Experimental validation demonstrated that the proposed architecture enables the robot to follow a person in real time, maintaining the predefined following distance of 1.3 m in each of the five conducted trials. The IMC-based controller demonstrated superior performance compared to the pole placement controller across all evaluated metrics. (4) Conclusions: The proposed control architecture effectively addresses the challenges of human-following in indoor environments, offering a robust, real-time solution suitable for assistive robotics with limited computational resources. The system's modularity and scalability make it a promising approach for future developments in personal assistance robotics. [ABSTRACT FROM AUTHOR]

Recent studies have shown that electrical systems in wind power conversion are subject to a 67 % failure rate, and conventional three-phase generators are considered to be the most sensitive to these faults. Induction generator current harmonics are a major source of torque ripples causing acoustic noise and vibrations. These ripples can progressively increase under faulty operating conditions. In six-phase machines, there is appearance of high harmonic currents in the air gap as space harmonics. Power electronic converters are also subject to faults, the most common being Short-Circuit (SC) and Open-Circuit (OC) faults. SC faults cause large peaks in the line currents, which trigger the protection devices, resulting in a complete shutdown of the production system. OC faults, on the other hand, cause imbalances in the phases, but the system remains in operation. Direct Torque Control (DTC) based on Space Vector Modulation (SVM) at constant switching frequency is an effective solution to tackle induction generator current harmonics. This paper proposes a DTC combined with a Proportional Integral Fuzzy Logic Controller (PIFLC) optimized by Particle Swarm Optimization (PSO) in order to overcome the problems of torque ripples. Furthermore, using Vector Space Decomposition (VSD) and the Modified Space Vector Pulse Width Modulation (MSVPWM) strategy, a significant reduction of harmonics in the air-gap can be achieved. Simulations were carried out under MATLAB/Simulink, and the results obtained demonstrate the superiority of the proposed DTC-PIFLC-PSO controller in terms of robustness against wind speed variations and under faulty switch conditions in the power converter of the Dual Star Induction Generator (DSIG) generator. In addition, a comparative study with the classical PI controller is presented to show the effectiveness of the DTC-PIFLC-PSO controller against faults with a significant reduction in the Total Harmonic Distortion (THD) during both faulty and non-faulty conditions. [ABSTRACT FROM AUTHOR]

This study presents the design and implementation of a wire-driven, multi-joint robotic arm equipped with a cutting and gripping mechanism for harvesting delicate strawberries, with the goal of reducing labor and costs. The arm is mounted on a lifting mechanism and linked to a laterally movable module, which is affixed to the tube cultivation shelf. The trained deep learning model can instantly detect strawberries, identify optimal picking points, and estimate the contour area of fruit while the mobile platform is in motion. A two-stage fuzzy logic control (2s-FLC) method is employed to adjust the length of the arm and bending angle, enabling the end of the arm to approach the fruit picking position. The experimental results indicate a 90% accuracy in fruit detection, an 82% success rate in harvesting, and an average picking time of 6.5 s per strawberry, reduced to 5 s without arm recovery time. The performance of the proposed system in harvesting strawberries of different sizes under varying lighting conditions is also statistically analyzed and evaluated in this paper. [ABSTRACT FROM AUTHOR]

In the complex environment of microgrid deployments targeted at geographic regions, the seamless integration of renewable energy sources meets a variety of essential challenges. These include the unpredictable nature of renewable energy, characterized by intermittent energy generation, as well as ongoing fluctuations in load demand, the vulnerabilities present in distribution network failures, and the unpredictability that results from unfavorable weather conditions. These unexpected events work together to disturb the delicate balance between energy supply and demand, raising the alarming threat of system instability and, in the worst cases, the sudden advent of damaging blackouts. To address this issue, a fuzzy logic-based energy management system has been developed to monitor, manage, and optimize energy consumption in microgrids. This study focuses on the control of diesel generators and utility grids in a grid-connected microgrid which manages and evaluates numerous energy consumption and distribution features within a specified system, e.g., building or a microgrid. An energy management system is suggested based on fuzzy logic as a swift fix for complications with effective and competent resource management, and its presentation is compared with both the grid-connected and off-grid modes of the microgrid. In the end, the results exhibit that the proposed controller outclasses the predictable controllers in dropping sudden variations that arise during the addition of sources of renewable energy, supporting the refurbishment of the constant system. [ABSTRACT FROM AUTHOR]

This paper proposes a new design method of energy management strategy (EMS) with adaptive super-twisting sliding mode control (ASTSMC) for fuel cell/battery/supercapacitor hybrid vehicle (FCHEV). The main objective of the proposed EMS is to improve power performance, fuel cell lifetime, and fuel consumption while considering the regulation of the DC-bus voltage. The proposed EMS is designed based on a frequency-decoupling technique using an adaptive low-pass filter, Harr wavelet transform (HWT), and FLC to decouple the required power into low, medium, and high-frequency components for fuel cell, battery, and supercapacitor, respectively. The presented frequency-decoupling-based strategy can improve the power performance of the vehicle as well as reduce load stress and power fluctuation in the fuel cell. Nevertheless, the neural network optimization algorithm (NNOA) is employed to optimize the membership functions of FLCs while considering the hydrogen consumption and constraints on the state of charge (SOC) of the battery and supercapacitor. To achieve robustness and high precision control, the ASTSMC is developed based on a nonlinear disturbance observer (NDOB) to stabilize the DC-bus voltage and currents of the energy sources, ensuring that the fuel cell, battery, and supercapacitor track their obtained reference values. The FCHEV system with the proposed EMS is modeled on MATLAB/Simulink, and three typical driving cycles such as HWFET, UDDS, and WLTP driving schedules are used for evaluation. The findings exhibit that the proposed EMS can effectively improve the fuel economy, reduce power fluctuation in the fuel cell, and prolong its lifetime compared to other existing methods such as the equivalent consumption minimization strategy (ECMS), state machine (SM), and FLC-based EMSs. [ABSTRACT FROM AUTHOR]

In the development of decarbonization technologies and renewable energy, water electrolysis has emerged as a key technology. The efficiency of hydrogen production and its applications are significantly affected by power stability. Enhancing power stability not only improves hydrogen production efficiency and reduces maintenance costs but also ensures long-term reliable system operation. This study proposes a thermal control method that stabilizes hydrogen output by precisely adjusting the temperature of the electrolysis stack, thereby improving hydrogen production efficiency. Fluctuations in the electrolysis stack temperature can lead to instability in the hydrogen output and energy utilization, negatively affecting overall hydrogen production. To address this issue, this study introduces an innovative system architecture and a novel thermal control strategy combining fuzzy logic control with a long short-term memory neural network. This method predicts and adjusts the flow rate of chilled water to maintain the electrolysis stack temperature within a range of ±1 °C while sustaining a constant power output of 10 kW. This approach is crucial for ensuring system stability and maximizing hydrogen production efficiency. Long-term experiments have validated the effectiveness and reliability of this method, demonstrating that this thermal control strategy not only stabilizes the hydrogen production process but also increases the volume of hydrogen generated. [ABSTRACT FROM AUTHOR]

The growing use of proton-exchange membrane fuel cells (PEMFCs) in hybrid propulsion systems is aimed at replacing traditional internal combustion engines and reducing greenhouse gas emissions. Effective power distribution between the fuel cell and the energy storage system (ESS) is crucial and has led to a growing emphasis on developing energy management systems (EMSs) to efficiently implement this integration. To address this goal, this study examines the performance of a fuzzy logic rule-based strategy for a hybrid fuel cell propulsion system in a small hydrogen-powered passenger vessel. The primary objective is to optimize fuel efficiency, with particular attention on reducing hydrogen consumption. The analysis is carried out under typical operating conditions encountered during a river trip. Comparisons between the proposed strategy with other approaches—control based, optimization based, and deterministic rule based—are conducted to verify the effectiveness of the proposed strategy. Simulation results indicated that the EMS based on fuzzy logic mechanisms was the most successful in reducing fuel consumption. The superior performance of this method stems from its ability to adaptively manage power distribution between the fuel cell and energy storage systems. [ABSTRACT FROM AUTHOR]

Introduction. Weather changes lead to create oscillations in values of power extracted from renewable energy resources (RERs). These power oscillations pose significant challenges in RERs integration process with the power grid systems, through its effects on power system stability. Many studies have been performed in various methods to mitigate the output power fluctuation of wind power generation system (WPGS). Purpose. This study focuses on increasing the mitigation rate of the output power fluctuation of WPGS caused by the rapid wind speed changes during wind gusts. Superconducting magnetic energy storage (SMES) system through its properties represents an effective solution for the WPGS power fluctuation issue. WPGS and SMES systems are linked to power grid system through the point of common coupling (PCC). Methods. This paper proposes two robust controllers for controlling the SMES system. The first controller is a Fuzzy Logic Controller (FLC), which has been utilized for controlling the power exchange between the SMES coil and the PCC of the utility grid. While the second controller is a PI controller, which have been utilized to regulate the voltages between the two sides of the PCC and the DC link capacitor in the SMES system. The proposed controllers have been constructed so that can make the SMES system absorb/deliver the real power instantaneously from/toward PCC according the wind speed changes. MATLAB/Simulink has been utilized to simulate the system under study and test the performance of proposed controllers. In addition, two different wind speed scenarios have been used in the simulation. Practical value. Results of simulation have proven the effectiveness of proposed controllers so that the active power fluctuation delivered to utility grid can be reduced by up to 89 %. References 31, tables 4, figures 9.

Abstract This paper aims to increase the Unmanned Aerial Vehicle's (UAV) capacity for target tracking. First, a control model based on fuzzy logic is created, which modifies the UAV's flight attitude in response to the target's motion status and changes in the surrounding environment. Then, an edge computing-based target tracking framework is created. By deploying edge devices around the UAV, the calculation of target recognition and position prediction is transferred from the central processing unit to the edge nodes. Finally, the latest Vision Transformer model is adopted for target recognition, the image is divided into uniform blocks, and then the attention mechanism is used to capture the relationship between different blocks to realize real-time image analysis. To anticipate the position, the particle filter algorithm is used with historical data and sensor inputs to produce a high-precision estimate of the target position. The experimental results in different scenes show that the average target capture time of the algorithm based on fuzzy logic control is shortened by 20% compared with the traditional proportional-integral-derivative (PID) method, from 5.2 s of the traditional PID to 4.2 s. The average tracking error is reduced by 15%, from 0.8 m of traditional PID to 0.68 m. Meanwhile, in the case of environmental change and target motion change, this algorithm shows better robustness, and the fluctuation range of tracking error is only half of that of traditional PID. This shows that the fuzzy logic control theory is successfully applied to the UAV target tracking field, which proves the effectiveness of this method in improving the target tracking performance.

Abstract This paper presents a comparative study between four techniques recently used to improve the wind energy conversion system (WECS) to water pumping systems. The WECS is a renewable energy source which has developed rapidly in recent years. The use of the WECS in the water pumping field is a free solution (economically) compared to the use of the electricity grid supply. The control of WECS, equipped with a permanent magnet synchronous generator, has the objective of carefully maximising power generation. A comparative study between the proposed Fuzzy Logic Control, optimised using a genetic algorithm and particle swarm optimisation algorithm, and the conventional Perturb and Observe MPPT method using Matlab/Simulink, is presented. The performance of the proposed system has been verified against the generated output voltage, current and power waveforms, intermediate circuit voltage waveform, and generator speed. The presented results demonstrate the effectiveness of the control strategy applied in this work.

The coupling of photovoltaic power generation with water electrolyzer is advantageous for enhancing solar energy utilization and generating green hydrogen. In this work, an off-grid photovoltaic-based hydrogen production system consisting of photovoltaic, electrolyzer, battery energy storage system and supercapacitor was developed. A coordinated operation strategy is designed to manage the power of each unit in the system to avoid significant fluctuations in working power and frequent start-stop operations of the electrolyzer unit. Firstly, the coordinated operation strategy sets five operation modes for the photovoltaic-based hydrogen production system, and the fuzzy logic control algorithm is used to choose the operation mode to determine the reference power of each unit. On this basis, the state of charge feedback-based first-order low-pass filter algorithm is used to achieve the power allocation to mitigate deep charging and discharging of the supercapacitor. The proposed solution is extensively assessed through comparative simulation experiments based on three typical test days. The numerical results confirm its effectiveness and demonstrate the potential for achieving high system efficiency. [Display omitted] • Five operation modes for off-grid PV hydrogen production system is proposed. • Fuzzy logic control algorithm is used to choose the operation mode. • The SOC feedback-based FLF algorithm is used to achieve the power allocation. • Hydrogen production is improved and deep charging and discharging of SC is mitigated. [ABSTRACT FROM AUTHOR]

In large buildings, effective load shedding and shifting and providing the maximum power through solar renewable sources remain challenges because of users' unpredictable load consumption. Conventionally, load shifting, load shedding, and load covering are majorly dependent on user inputs. The lack of user interest in participating in demand responses for effective load shifting and covering remains a problem. Effective load covering through renewables and user-friendly load shedding and shifting with maximized user participation are challenging and demand high-resolution user load consumption information, which are not possible without sophisticated communication and digital twins. In this research work, a novel fuzzy-logic-based cascaded decentralized load-controlling mechanism has been developed that manages the residential building load through load-shifting, load-covering, and load-shedding schemes without any communication protocols and digitization between residential units. The decentralized controller aims to effectively utilize the centralized resources of power generation with the effective automated participation of users. The quantification of the load shifting, covering, and shedding performed during peak hours was well covered under the load-covering scheme, and the results showed that flexibility capacities of 1617 kW were achieved for load covering, 294 kW for load shedding, and 166.34 kW through shifting. A total load of 60 kW, which was reduced during shedding and shifting, was well covered during load covering through renewables. [ABSTRACT FROM AUTHOR]

The power transformers are the important part of electrical networks where transformer reliability and operational lifetime depends on sufficiently accurate and reliable protective means. Other traditional forms of differential protection that were developed initially also suffer from the inability to distinguish between a fault and normal operation such as inrush currents in transformers and CT saturation. This paper presents the development of an improved differential relay augmented by Fuzzy-Logic Control System (FLC), to improve (a) dependability, (b) performance of the existing transformer protection systems, and (c) accuracy in fault identification possible due to uncertainty and non-linearity in transformer operation. They include the proposed methodology compared to the traditional Rule-based current differential method in outlining the protection settings. MATLAB/Simulink model of the power transformer and protection methods suggested in the study form a part of the investigation. Computer simulations show that the presented scheme provides a substantial increase in the speed and resolution of fault detection and fault types identification relating to current differential method based on the Rule. The system's accuracy rate is the average of 98% for internal faults and 95% for external faults while its response time is 25ms for internal faults and 30ms for external faults. Furthermore, the Fuzzy-Logicbased system has an 90% efficiency in detect the defect and 85% efficiency in identify the inrush currents. The findings of this research prove that the differential relay based on Fuzzy-Logic enhances the flexibility and reliability of transformer protection and opens the road to the introduction of further improvements in the intelligent protection systems in the future. [ABSTRACT FROM AUTHOR]

Copyright of Przegląd Elektrotechniczny is the property of Przeglad Elektrotechniczny and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Introduction. Weather changes lead to create oscillations in values of power extracted from renewable energy resources (RERs). These power oscillations pose significant challenges in RERs integration process with the power grid systems, through its effects on power system stability. Many studies have been performed in various methods to mitigate the output power fluctuation of wind power generation system (WPGS). Purpose. This study focuses on increasing the mitigation rate of the output power fluctuation of WPGS caused by the rapid wind speed changes during wind gusts. Superconducting magnetic energy storage (SMES) system through its properties represents an effective solution for the WPGS power fluctuation issue. WPGS and SMES systems are linked to power grid system through the point of common coupling (PCC). Methods. This paper proposes two robust controllers for controlling the SMES system. The first controller is a Fuzzy Logic Controller (FLC), which has been utilized for controlling the power exchange between the SMES coil and the PCC of the utility grid. While the second controller is a PI controller, which have been utilized to regulate the voltages between the two sides of the PCC and the DC link capacitor in the SMES system. The proposed controllers have been constructed so that can make the SMES system absorb/deliver the real power instantaneously from/toward PCC according the wind speed changes. MATLAB/Simulink has been utilized to simulate the system under study and test the performance of proposed controllers. In addition, two different wind speed scenarios have been used in the simulation. Practical value. Results of simulation have proven the effectiveness of proposed controllers so that the active power fluctuation delivered to utility grid can be reduced by up to 89 %. References 31, tables 4, figures 9. [ABSTRACT FROM AUTHOR]

This paper introduces a novel fuzzy logic switched model predictive control (MPC) algorithm for articulated steering vehicles, addressing significant path tracking challenges due to varying road conditions and vehicle speeds. Traditional single-model and parameter-based controllers struggle with tracking errors and computational inefficiencies under diverse operational conditions. Therefore, a kinematics-based MPC algorithm is first developed, showing strong real-time performance but encountering accuracy issues on low-adhesion surfaces and at high speeds. Then, a 4-DOF dynamics-based MPC algorithm is designed to enhance tracking accuracy and control stability. The proposed solution is a switched MPC strategy, integrating a fuzzy control system that dynamically switches between kinematics-based and dynamics-based MPC algorithms based on error, solution time, and heading angle indicators. Subsequently, simulation tests are conducted using SIMULINK and ADAMS to verify the performance of the proposed algorithm. The results confirm that this fuzzy-based MPC algorithm can effectively mitigate the drawbacks of single-model approaches, ensuring precise, stable, and efficient path tracking across diverse adhesion road conditions. [ABSTRACT FROM AUTHOR]

Efficient thermal management of lithium-ion battery, working under extremely rapid charging-discharging, is of widespread interest to avoid the battery degradation due to temperature rise, resulting in the enhanced lifespan. Herein, thermal management of lithium-ion battery has been performed via a liquid cooling theoretical model integrated with thermoelectric model of battery packs and single-phase heat transfer. Aiming to alleviate the battery temperature fluctuation by automatically manipulating the flow rate of working fluid, a nominal model-free controller, i.e., fuzzy logic controller is designed. An optimized on-off controller based on pump speed optimization is introduced to serve as the comparative controller. Thermal control simulations are conducted under regular operating and extreme operating conditions, and two controllers are applied to control battery temperature with proper intervals which is conducive to enhance the battery charge-discharge efficiency. The results indicate that, for any operating condition, the fuzzy logic controller shows excellence in terms of the tracking accuracy of set-point of battery temperature. Thanks to the establishment of fuzzy set and fuzzy behavioral rules, the battery temperature has been throughout maintained near the set point, and the temperature fluctuation amplitude is highly reduced, with better temperature control accuracy of ∼0.2°C (regular condition) and ∼0.5°C (extreme condition) compared with ∼1.1°C (regular condition) and ∼1.6°C (extreme condition) of optimized on-off controller. While in the case of extreme operating condition, the proposed optimized on-off controller manifests the hysteresis in temperature fluctuation, which is ascribed to the set of dead-band for the feedback temperature. The simulation results cast new light on the utilization and development of model-free temperature controller for the thermal management of lithium-ion battery. [ABSTRACT FROM AUTHOR]

This paper aims to increase the Unmanned Aerial Vehicle's (UAV) capacity for target tracking. First, a control model based on fuzzy logic is created, which modifies the UAV's flight attitude in response to the target's motion status and changes in the surrounding environment. Then, an edge computing-based target tracking framework is created. By deploying edge devices around the UAV, the calculation of target recognition and position prediction is transferred from the central processing unit to the edge nodes. Finally, the latest Vision Transformer model is adopted for target recognition, the image is divided into uniform blocks, and then the attention mechanism is used to capture the relationship between different blocks to realize real-time image analysis. To anticipate the position, the particle filter algorithm is used with historical data and sensor inputs to produce a high-precision estimate of the target position. The experimental results in different scenes show that the average target capture time of the algorithm based on fuzzy logic control is shortened by 20% compared with the traditional proportional-integral-derivative (PID) method, from 5.2 s of the traditional PID to 4.2 s. The average tracking error is reduced by 15%, from 0.8 m of traditional PID to 0.68 m. Meanwhile, in the case of environmental change and target motion change, this algorithm shows better robustness, and the fluctuation range of tracking error is only half of that of traditional PID. This shows that the fuzzy logic control theory is successfully applied to the UAV target tracking field, which proves the effectiveness of this method in improving the target tracking performance. [ABSTRACT FROM AUTHOR]

This paper presents a comparative study between four techniques recently used to improve the wind energy conversion system (WECS) to water pumping systems. The WECS is a renewable energy source which has developed rapidly in recent years. The use of the WECS in the water pumping field is a free solution (economically) compared to the use of the electricity grid supply. The control of WECS, equipped with a permanent magnet synchronous generator, has the objective of carefully maximising power generation. A comparative study between the proposed Fuzzy Logic Control, optimised using a genetic algorithm and particle swarm optimisation algorithm, and the conventional Perturb and Observe MPPT method using Matlab/Simulink, is presented. The performance of the proposed system has been verified against the generated output voltage, current and power waveforms, intermediate circuit voltage waveform, and generator speed. The presented results demonstrate the effectiveness of the control strategy applied in this work. [ABSTRACT FROM AUTHOR]

The integration of renewable energy sources (RESs), is a workable way to meet operational demands and boosting industrial activities' efficiency, sustainability, reduce and environmental effect, in particular, in the field of pumping systems. This study looks into the effective dynamic integration of wind turbines and photovoltaic (PV) systems to drive an induction motor (IM) under varying environmental conditions. Results show that the integrated system's power output grows in direct proportion to the intensity of solar radiation and wind speed, which enables the IM to operate more efficiently and accelerate. The study's cutting-edge control systems, in particular, operate very well by utilising sliding mode control (SMC) to keep the rotor flux within safe operating limits, hence avoiding magnetic saturation and possible motor failures. The study also emphasises how well the reference speed adjustment (RSA) system works to stabilise the DC link voltage with fuzzy logic approaches, guaranteeing that the IM's generated power is used as efficiently as possible while avoiding undesired fluctuations. [ABSTRACT FROM AUTHOR]

In order to solve the problem of inconsistent energy in the charging and discharging cycles of lithium-ion battery packs, a new multilayer equilibrium topology is designed in this paper. The structure adopts a hierarchical structure design, which includes intra-group equilibrium, primary inter-group equilibrium and secondary intergroup equilibrium. This structure greatly increases the number of equilibrium paths for lithium-ion batteries, thus shortening the time required for equilibrium, and improving the overall efficiency. In terms of control strategy, fuzzy logic control (FLC) is chosen to control the size of the equilibrium current during the equilibrium process. We performed rigorous modeling and simulation of the proposed system by MATLAB and Simulink software. Experiments show that the multilayer equilibrium circuit structure greatly exceeds the traditional single-layer equilibrium circuit in terms of efficacy, specifically, the Li-ion battery equilibrium speed is improved by 12.71% in static equilibrium, 14.48% in charge equilibrium, and 11.19% in discharge equilibrium. In addition, compared with the maximum value algorithm, the use of the FLC algorithm reduces the equalization time by about 3.27% and improves the energy transfer efficiency by about 66.49% under the stationary condition, which verifies the feasibility of the equalization scheme. [ABSTRACT FROM AUTHOR]

Object tracking is one of the major tasks for mobile robots in many real-world applications. Also, artificial intelligence and automatic control techniques play an important role in enhancing the performance of mobile robot navigation. In contrast to previous simulation studies, this paper presents a new intelligent mobile robot for accomplishing multi-tasks by tracking red-green-blue (RGB) colored objects in a real experimental field. Moreover, a practical smart controller is developed based on adaptive fuzzy logic and custom proportional-integral-derivative (PID) schemes to achieve accurate tracking results, considering robot command delay and tolerance errors. The design of developed controllers implies some motion rules to mimic the knowledge of experienced operators. Twelve scenarios of three colored object combinations have been successfully tested and evaluated by using the developed controlled image-based robot tracker. Classical PID control failed to handle some tracking scenarios in this study. The proposed adaptive fuzzy PID control achieved the best accurate results with the minimum average final error of 13.8 cm to reach the colored targets, while our designed custom PID control is efficient in saving both average time and traveling distance of 6.6 s and 14.3 cm, respectively. These promising results demonstrate the feasibility of applying our developed image-based robotic system in a colored object-tracking environment to reduce human workloads. [ABSTRACT FROM AUTHOR]

In order to solve the problem of low efficiency of traditional balanced electric topology, a two-level balanced topology is proposed. This balancing topology divides the battery pack into two forms: Intra-group and inter-group. Buck-Boost equalization circuit is used in the group, and reconfigurable equalization circuit is used between the groups. The intra-group and inter-group can be equalized at the same time, which improves the equalization efficiency. Taking SOC(State of Charge) as the equalization variable, the intra-group equalization algorithm adopts the fuzzy logic control strategy based on SOC to reduce the equalization time and improve the equalization efficiency. MATLAB/Simulink software is used to model and simulate the circuit topology and the results are compared with the traditional Buck-Boost circuit. The simulation results show that compared with the traditional Buck-Boost circuit, the proposed algorithm and equalization topology reduce the equalization time by about 28% under the charge-discharge state, indicating that the equalization circuit and algorithm have good performance. [ABSTRACT FROM AUTHOR]

As a typical nonlinear control method, backstepping control can decouple the mathematical model of permanent magnet synchronous motors. In addition, permanent magnet synchronous motor control systems based on the backstepping method can enhance the control performance of the control system to a certain extent. Furthermore, the design step is easy and simple to implement in engineering practice. However, the long-term wear and tear, aging, high temperature caused by changes in the basic parameters of motor and external load sudden changes as well as other factors will bring interference to the control system, leading to reduced-control accuracy and control performance degradation. To solve this problem, this paper suggests a control strategy combining backstepping control and fuzzy control based on backstepping control. It sets the load adaptive law and utilizes fuzzy control to make online real-time adjustments to the control parameters in the backstepping control. This is done to improve the immunity of interference and stability of the control system in response to the changes in the parameters of the body of the motor and sudden changes of the load. The effectiveness and feasibility of this system is verified by MATLAB simulation and experimental results, which provides a feasible solution for permanent magnet synchronous motor immunity and high-precision control occasions. [ABSTRACT FROM AUTHOR]

Copyright of Przegląd Elektrotechniczny is the property of Przeglad Elektrotechniczny and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Photovoltaic (PV) electricity is a single of more useful forms of clean energy which is seen as being kinder to the environment. The sole drawback of solar photovoltaic (PV) systems is their inefficient energy conversion, which makes it increasingly crucial to extract as much electricity as possible from them. External factors have an impact on the Photovoltaics Current-Voltage (I-V) then and Power-Voltage (P-V) characteristics since they are nonlinear and alter in reaction to temperature and solar light. An electrical circuit known as an MPP tracker (MPPT) is applied to avoid power losses. The literature has a large number of developed and published MPP tracking techniques. Performance comparisons are covered in this study along with the traditional approach (Perturb & Observe) and the more advanced techniques (Fuzzy Logic Control and Sliding Mode Control).

Thermal comfort strategies represent a very important aspect when it comes to achieving thermal comfort conditions. At the same time, recently, there has been a growing interest in user-centered building control concepts. Thus, this work focuses on developing a thermal control strategy that combines the restrictions related to achieving thermal comfort, expressed in terms of environmental parameters and specific factors of personal perception, with the objective of reducing energy consumption. This case study aims at implementing this strategy in a laboratory room located within the Technical University of Civil Engineering Bucharest. The strategy proposed by the authors is based on implementing a combination of a Model Predictive Control (MPC) model and a fuzzy system, which presents constraints related to the room occupancy level. Relevant observations regarding the parameterization of fuzzy systems are also highlighted. [ABSTRACT FROM AUTHOR]

With the gradual increase in wind power installation capacity, the proportion of traditional synchronous generators driven by fossil fuel is gradually declining. Due to the fact that wind turbines are connected to the grid through power electronic converters, which decouple rotor speeds from the system frequency and reduce system inertia levels, inadequate inertia levels can pose a threat to frequency stability when disturbances occur. To address this issue, this paper proposes a frequency regulation optimization strategy for the direct current (DC) transmission of a wind storage system. This strategy incorporates virtual inertia control and virtual droop control to adjust wind power output based on frequency deviation and rate of change. Fuzzy logic control is employed for energy storage, adaptively adjusting active power based on frequency deviation and the rate of change. Additionally, under the context of multi-DC transmission in renewable energy systems, an optimization strategy for proportion and integration (PI) parameters of the frequency limit controller (FLC) is proposed. Considering frequency deviation and DC regulation power simultaneously, the double deep Q-network (DDQN) algorithm is adopted in the simulation model to attain the optimal parameters of FLC. Simulation results conducted using MATLAB/Simulink 2022a indicate that this strategy increases the lowest frequency by 0.28 Hz and decreases the response time by 1.04 s compared with the non-optimized strategy. [ABSTRACT FROM AUTHOR]

This study introduced a novel control approach based on fuzzy logic control (FLC) to enhance the frequency regulation capacity of variable-speed wind turbines (VSWTs). The proposed method integrates FLC within droop and inertia control loops. Real-time measurements of the system frequency and the rate of change of frequency (ROCOF) serve as inputs to the FLC, enabling the method to improve the frequency response by VSWTs. In addition, the method employs FLC for pitch angle frequency control, optimizing reserve power for frequency regulation under varying wind speed levels. The innovative aspect of this study lies in the simultaneous application of FLC to pitch angle frequency control and droop/inertia control, leading to the enhanced frequency regulation capability of VSWTs and smoother operation across a range of wind speeds. Compared with traditional methods, the proposed approach provides a comprehensive and effective solution to the challenges associated with frequency regulation in VSWTs. Through simulations across different wind speed scenarios, the proposed control method demonstrated the best performance among various mature methods, highlighting the efficacy of the proposed method on the frequency regulation of VSWTs under different wind speeds. This study's findings highlight the potential of the proposed FLC-based method to optimize frequency regulation and contribute to more reliable and efficient wind energy systems. [ABSTRACT FROM AUTHOR]

Ejector-based proton exchange membrane fuel cells (PEMFCs) are of great interest due to their simplicity and feasibility. Thus, proton exchange membrane fuel cells are considered the most suitable technology for in-vehicle systems, industrial applications, etc. Despite the passive characteristics of the ejector, active control of the hydrogen supply system is needed to ensure sufficient hydrogen, maintain the stack pressure, and ensure effective entrainment. In this research, a novel semi-empirical model is proposed to accurately predict the entrainment performance of the ejector with an 80 kW fuel cell system. According to the precise semi-empirical model, the hydrogen supply system and the anode channel are modeled. Then, a fuzzy logic controller (FLC) is developed to supply sufficient and adequate gas flow and maintain the rapid dynamic response. Compared to the conventional proportional–integral–derivative controller, the fuzzy logic controller could reduce the anode pressure variability by 5% during a stepped case and 2% during a dynamic case. [ABSTRACT FROM AUTHOR]

The electric drive thermal management system of electric vehicles is an important part of electric vehicles. To improve the cooling effect of the electric drive system and reduce energy consumption, a cooling scheme combining the spray oil cooling of the motor end winding with the cooling water jacket of the motor shell and the water cooling in the motor shaft is proposed. Based on the electric drive cooling system framework of electric vehicles, a fuzzy logic control strategy is established. The flow rate and heat dissipation in the system are optimized by controlling the rotation speed of the oil/water cooling pump and the radiator fan, which further reduces the energy consumption of the system. In addition, through the co-simulation of AMESim and Simulink, under the same simulation time, the simulation comparison and analysis are carried out under SFTP-US06 and WLTC conditions, respectively. The power loss of fuzzy logic control is 9% and 28% lower than that of switching control, and 21% and 25% lower than that of PID control, respectively. [ABSTRACT FROM AUTHOR]

This work presents a new fuzzy logic parameter tuning-based fractional order sliding mode control (F-FOSMC) strategy for the trajectory tracking of robotic systems. The primary objective is to achieve an optimal control compromise seeking to minimize energy consumption for required motions, all while increasing both accuracy and rapidity of the transient response, addressing therefore a critical challenge in the field of robotics. Our approach integrates a sophisticated fuzzy logic fractional order mechanism to dynamically fine-tune the noninteger derivation parameter of the FOSMC, offering adaptability to varying operational conditions. This intelligent fuzzy logic controller tuning optimizes the FOSMC performance, demonstrates a remarkable capacity to handle uncertainties and disturbances intrinsic to robotic applications and allows to optimize the trade-off between the energy consumption and transient response efficiency throughout the robot's motion. The effectiveness of the proposed study is thoroughly examined via simulations conducted on a 3 DOF manipulator robotic system. Results showcase that F-FOSMC significantly reduces control energy consumption while preserving rapid and efficient transient response characteristics. [ABSTRACT FROM AUTHOR]

This research paper focuses on the application of fuzzy logic control in the high-frequency switching control of SEPIC DC/DC converters and three-phase inverters for photovoltaic systems. The research is separated into two distinct sections. The first part entails the design and development of a prototype mechanism for a single-ended primary-inductor converter (SEPIC), which is utilized to manage the input voltage from the PV system. The operation of this SEPIC DC/DC converter incorporates real-time fuzzy logic control principles and methods. The second part involves the design and creation of a three-phase inverter mechanism (SVPWM) responsible for driving a three-phase induction motor. In a similar manner, fuzzy logic control principles and methods are independently employed for the operation control of this three-phase inverter. Both control mechanisms are integrated with the mathematical model of MATLAB/Simulink program and synchronized with the TMS320F28379D microcontroller in order to effectively regulate their respective functions. The evaluation and experimentation of the developed mechanisms demonstrate the successful regulation of input and output voltages for the SEPIC DC/DC Converter, as well as the efficient control of the three-phase induction motor's speed, torque, and power using the three-phase inverter. [ABSTRACT FROM AUTHOR]

The numerical solution of multibody systems is not a straightforward problem. The formulation of the equations of motion is augmented with the constraint equations that lead to a set of differential algebraic equations (DAEs). These constraints govern the relative motion between the system's components at the position level (geometric constraints) and may restrict the velocity of particular components (rolling constraints). There are several factors that determine the effectiveness of numerical integration methods and the extent of their applicability owing to the various motion circumstances. These factors include numerical stability throughout the integration and computation time, as well as allowable error percentage and the length of simulation time. In this regard, this research examines existing approaches for constraint stabilization during numerical integration and introduces a new methodology based on fuzzy control algorithm, whose coefficients are independent of the dynamic characteristics of different systems. Schematics of the new methodology are presented; two examples of spatial multibody systems with holonomic and nonholonomic constraints are solved to evaluate the effectiveness of the proposed method. It can be concluded that fuzzy control contributes an excellent solution for generic system configuration and is suitable for lengthy simulations with minimal computation time. [ABSTRACT FROM AUTHOR]

This paper explores the implementation and possibilities of utilizing fuzzy logic theory for optimal operation and early fault detection in marine diesel engines. It emphasizes its role in managing the complexity and ambiguity inherent in engine performance and preventive maintenance. Preventive maintenance is crucial for ensuring the reliability and longevity of marine diesel engines. Implementing fuzzy logic control (FLC) systems can enhance the preventive maintenance strategies for these engines, leading to reduced downtime, lower maintenance costs, and improved overall performance. Through a comprehensive literature review and analysis of a case study, this paper demonstrates the adaptability, effectiveness, and transformative potential of fuzzy logic systems. Focusing on applications such as engine speed control, performance improvements, and early fault detection, the paper highlights the implementation of fuzzy logic for enhanced predictive capabilities. The study aims to offer a flexible approach to engine management through fuzzy logic, laying the way for significant improvement in optimal marine diesel engine operation. [ABSTRACT FROM AUTHOR]

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The recent surge in biosignal-based control signifies a profound paradigm shift in biomedical engineering. This innovative approach has injected new life into control theory, ushering in advancements in human-body interaction and control. Surface Electromyography (sEMG) emerges as a pivotal biosignal, attracting considerable attention for its wide-ranging applications across medicine, science, and engineering, particularly in the domain of functional rehabilitation. This study delves into the use of sEMG signals for controlling a robotic arm, with the overarching aim of improving the quality of life for people with disabilities in Vietnam. Raw sEMG signals are acquired via appropriate sensors and subjected to a robust processing methodology involving analog-to-digital conversion, band-pass and low-pass filtering, and envelope detection. To demonstrate the efficacy of the processed sEMG signals, this study introduces a robotic arm model capable of mimicking intricate human finger movements. Employing a fuzzy logic control strategy, the robotic arm demonstrates successful operation in experimental trials, characterized by swift response times, thereby positioning it as a valuable assistive device for people with disabilities. This investigation not only validates the feasibility of sEMG-based control for robotic arms, but also underscores its potential to significantly improve the lives of individuals with disabilities, a demographic that represents a substantial portion (approximately 8%) of the Vietnamese population. [ABSTRACT FROM AUTHOR]

This research work introduces a power management system for a hybrid energy storage system (PMHESS) configuration for urban electric vehicles utilizing a fuzzy logic controller (FLC). Consequently, the configuration includes two DC/DC interleaved converters that establish a connection between the battery and ultra-capacitors (UCs), thereby ensuring a substantial power capacity. The FLC is adaptable and sturdy, considering information from sources other than the vehicle, such as other vehicles or road infrastructure. The study explores incorporating road topography into the control structure to improve hybrid storage performance. Simulation results show that combining lithium batteries with UCs improves the energy source's performance and reliability. The power management algorithm reduces sudden demands on the battery by considering the slope of the ground. The work proposes energy storage integration for electric vehicles, exploring its benefits through simulations. Overall, the proposed hybrid energy storage system (HESS) demonstrates high efficiency and power for urban electric vehicles. The results are validated using MATLAB/Simulink. [ABSTRACT FROM AUTHOR]

A lithium-ion battery–ultracapacitor hybrid energy storage system (HESS) has been recognized as a viable solution to address the limitations of single battery energy sources in electric vehicles (EVs), especially in urban driving conditions, owing to its complementary energy features. However, an energy management strategy (EMS) is required for the optimal performance of the HESS. In this paper, an EMS based on the particle swarm optimization (PSO) of the fuzzy logic controller (FLC) is proposed. It aims to minimize battery current and power peak fluctuations, thereby enhancing its capacity and lifespan, by optimizing the weights of formulated FLC rules using the PSO algorithm. This paper utilizes the battery temperature as the cost function in the optimization problem of the PSO due to the sensitivity of lithium-ion batteries (LIBs) to operating temperature variations compared to ultracapacitors (UCs). An evaluation of optimized FLC using PSO and a developed EV model is conducted under the Urban Dynamometer Driving Schedule (UDDS) and compared with the unoptimized FLC. The result shows that 5.4% of the battery's capacity was conserved at 25.5 °C, which is the highest operating temperature attained under the proposed strategy. [ABSTRACT FROM AUTHOR]